How Do Manufacturing Firms Manage Artificial Intelligence to Drive Iterative Product Innovation?

In this article, we attempt to investigate how manufacturing firms can effectively manage artificial intelligence (AI) to deal with the tension posed by both the opportunities and risks associated with AI applications to drive iterative product innovation. We present empirical insights from three cases involving a typical Chinese manufacturing firm engaged in AI-driven iterative product innovation. We followed our sample firm for 12 months, relying on interviews, observations, and external archival data to collect rich data about its innovation process, and conducted text coding and text analytics to gain insights into the data. Our findings reveal that AI provides opportunities for broad, deep, and agile stakeholder interactions with the support of AI-enabled interactive digital platforms, intelligent manufacturing, and intelligent machines. During this process, risks emerge around data leakage, over-reliance on online intelligence decision-making, and unpredictable AI behaviors. Manufacturing firms need to manage AI by focusing on key principles relating to formulating guidelines for data management, integrating offline decision-makers' experience into online intelligence analysis, and establishing management standards for intelligent devices. We combine these insights into a framework to illustrate how manufacturing firms manage AI to facilitate progress in iterative product innovation.